Economics of wildfire suppression: Estimation of drivers of suppression expenditure and Risk preference experiments with wildfire management
Bibliographic record
Abstract
Public wildfire management agencies are presented with a momentous responsibility: to protect life, property and infrastructure from the devastation of wildland fire, while operating at a level of expenditure justifiable to taxpayers. At a time when climate change drives more extreme fire behaviour, agencies such as Alberta Wildfire must also be prepared to respond despite uncertain budgets. This thesis contributes by offering new empirical insights on wildfire suppression through two directions. Firstly, Chapter 2 focuses on the drivers of wildfire suppression costs. An empirical model seeks to explain how costs are affected by a series of environmental factors, such as time-variant weather variables and time-invariant landscape characteristics, as well as by operational policy variables based on Alberta Wildfire’s organizational capacity and priorities. Results from regression analysis and machine learning show that while policy decisions have measurable impacts on abating costs, the bulk of expenditures is driven by environmental factors. Chapter 3 studies risk aversion of Alberta Wildfire Incident Commanders (ICs). Through laboratory economic experiments, I seek to determine whether ICs, who are in a risky profession, exhibit risk preferences in laboratory experiments that are different from a control group. Results show that ICs’ experiment choices are not significantly different from those of typical experimental subjects across all risk elicitation tasks. However, among their colleagues, ICs with additional operational deployment experience tend to exhibit significantly lower levels of risk aversion. Findings from this study motivate further research into wildland firefighters’ risk preferences that will help decisionmakers better understand how individual risk perceptions impact resource allocation, and by extension, costs. Taken together, the novel insights generated from this thesis contribute to the multi-disciplinary field of wildfire suppression research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".